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Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2007, Article ID 47214, 11 pages doi:10.1155/2007/47214 Research Article Gene Systems Network Inferred from Expression Profiles in Hepatocellular Carcinogenesis by Graphical Gaussian Model Sachiyo Aburatani,1 Fuyan Sun,1 Shigeru Saito,2 Masao Honda,3 Shu-ichi Kaneko,3 and Katsuhisa Horimoto1 Biological Network Team, Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan Chemo & Bio Informatics Department, INFOCOM CORPORATION, Mitsui Sumitomo Insurance Surugadai Annex Building, 3-11, Kanda-Surugadai, Chiyoda-ku, Tokyo 101-0062, Japan Department of Gastroenterology, Graduate School of Medical Science, Kanazawa University, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan Received 28 June 2006; Revised 27 February 2007; Accepted May 2007 Recommended by Paul Dan Cristea Hepatocellular carcinoma (HCC) in a liver with advanced-stage chronic hepatitis C (CHC) is induced by hepatitis C virus, which chronically infects about 170 million people worldwide To elucidate the associations between gene groups in hepatocellular carcinogenesis, we analyzed the profiles of the genes characteristically expressed in the CHC and HCC cell stages by a statistical method for inferring the network between gene systems based on the graphical Gaussian model A systematic evaluation of the inferred network in terms of the biological knowledge revealed that the inferred network was strongly involved in the known genegene interactions with high significance (P < 10−4 ), and that the clusters characterized by different cancer-related responses were associated with those of the gene groups related to metabolic pathways and morphological events Although some relationships in the network remain to be interpreted, the analyses revealed a snapshot of the orchestrated expression of cancer-related groups and some pathways related with metabolisms and morphological events in hepatocellular carcinogenesis, and thus provide possible clues on the disease mechanism and insights that address the gap between molecular and clinical assessments Copyright © 2007 Sachiyo Aburatani et al This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited INTRODUCTION Hepatitis C virus (HCV) is the major etiologic agent of nonA non-B hepatitis, and chronically infects about 170 million people worldwide [1–3] Many HCV carriers develop chronic hepatitis C (CHC), and finally are afflicted with hepatocellular carcinoma (HCC) in livers with advanced-stage CHC Thus, the CHC and HCC cell stages are essential in hepatocellular carcinogenesis To elucidate the mechanism of hepatocellular carcinogenesis at a molecular level, many experiments have been performed from various approaches In particular, recent advances in techniques to monitor simultaneously the expression levels of genes on a genomic scale have facilitated the identification of genes involved in the tumorigenesis [4] Indeed, some relationships between the disease and the tumor-related genes were proposed from the gene expression analyses [5–7] Apart from the relationship between tumor-related genes and the disease at the molecular level, the information about the pathogenesis and the clinical characteristics of hepatocellular carcinogenesis has accumulated steadily [8, 9] However, there is a gap between the information about hepatocellular carcinogenesis at the molecular level and that at more macroscopic levels, such as the clinical level Furthermore, the relationships between tumorrelated genes and other genes also remain to be investigated Thus, an approach to describe the perspective of carcinogenesis from measurements at the molecular level is desirable to bridge the gap between the information at the two different levels Recently, we have developed an approach to infer a regulatory network, which is based on graphical Gaussian modeling (GGM) [10, 11] Graphical Gaussian modeling is one of the graphical models that includes the Boolean and Bayesian models [12, 13] Among the graphical models, GGM has the simplest structure in a mathematical sense; only the inverse of the correlation coefficient between the variables is needed, and therefore, GGM can be easily applied to a wide variety of data However, straightforward applications of statistical theory to practical data fail in some cases, and GGM also fails frequently when applied to gene expression profiles; here the expression profile indicates a set of the expression degrees of one gene, measured under various conditions This is because the profiles often share similar expression patterns, which indicate that the correlation coefficient matrix between the genes is not regular Thus, we have devised a procedure, named ASIAN (automatic system for inferring a network), to apply GGM to gene expression profiles, by a combination of hierarchical clustering [14] First, the large number of profiles is grouped into clusters, according to the standard approach of profile analysis [15] To avoid the generation of a nonregular correlation coefficient matrix from the expression profiles, we adopted a stopping rule for hierarchical clustering [10] Then, the relationship between the clusters is inferred by GGM Thus, our method generates a framework of gene regulatory relationships by inferring the relationships between the clusters [11, 16], and provides clues toward estimating the global relationships between genes on a large scale Methods for extracting biological knowledge from large amounts of literature and arranging it in terms of gene function have been developed Indeed, ontologies have been made available by the gene ontology (GO) consortium [17] to construct a functional categorization of genes and gene products, and by using the GO terms, the software determines whether any GO terms annotate a specified list of genes at a frequency greater than that expected by chance [18] Furthermore, various software applications, most of which are commercial software, such as MetaCore from GeneGo http://www.genego.com/, have been developed for the navigation and analysis of biological pathways, gene regulation networks, and protein interaction maps [19] Thus, advances in the processing of biological knowledge have enabled us to correspond to the results of gene expression analyses for a large amount of data with the biological functions In this study, we analyzed the gene expression profiles from the CHC and HCC cell stages, by ASIAN based on the graphical Gaussian Model, to reveal the framework of gene group associations in hepatocellular carcinogenesis For this purpose, first, the genes characteristically expressed in hepatocellular carcinogenesis were selected, and then, the profiles of the genes thus selected were subjected to the association inference method In addition to the association inference, which was presented by the network between the clusters, the network was further interpreted systematically by the biological knowledge of the gene interactions and by the functional categories with GO terms The combination of the statistical network inference from the profiles with the systematic network interpretation by the biological knowledge in the literature provides a snapshot of the orchestration of gene systems in hepatocellular carcinogenesis, especially for bridging the gap between the information on the disease mechanisms at the molecular level and at more macroscopic levels EURASIP Journal on Bioinformatics and Systems Biology 2.1 MATERIALS AND METHODS Gene selection We selected the up- and downregulated genes characteristically expressed in the CHC and HCC stages, as a prerequisite for defining the variables in the network inference by the graphical Gaussian modeling This involved the following steps (1) The averages and the standard deviations in the respective conditions, AV j and SD j , for j = 1, , Nc , are calculated (2) The expression degree of the ith gene in the jth condition, ei j , is compared with |AV j ± SD j | (3) The gene is regarded as a characteristically expressed gene, if the number of conditions that ei j ≥ |AV j ± SD j | is more than Nc /2 Although the criterion for a characteristically expressed gene is usually |AV j ± 2SD j |, the present selection procedure described above is simply designed to gather as many characteristically expressed genes as possible, and is suitable to capture a macroscopic relationship between the gene systems estimated by the following cluster analysis 2.2 Gene systems network inference The present analysis is composed of three parts: first, the profiles selected in the preceding section are subjected to the clustering analysis with the automatic determination of cluster number, and then the profiles of clusters are subjected to the graphical Gaussian modeling Finally, the network inferred by GGM is rearranged according to the magnitude of partial correlation coefficients, which can be regarded as the association strength, between the clusters The details of the analysis are as follows 2.2.1 Clustering with automatic determination of cluster number In clustering the gene profiles, here, the Euclidian distance between Pearson’s correlation coefficients of profiles and the unweighted pair group method using arithmetic average (UPGMA or group average method) were adopted as the metric and the technique, respectively, with reference to the previous analyses by GGM [11, 16] In particular, the present metric between the two genes is designed to reflect the similarity in the expression profile patterns between other genes as well as between the measured conditions, that is, n di j = ril − r jl , (1) l=1 where n is the total number of the genes, and ri j is the Pearson correlation coefficient between the i and j genes of the expression profiles that are measured at Nc conditions, pik , (k = 1, 2, , Nc ): ri j = l k=1 l k=1 pik − pi · p jk − p j pik − pi · l k=1 p jk − p j , (2) where pi is the arithmetic average of pik over Nc conditions Sachiyo Aburatani et al In the cluster number estimation, various stopping rules for the hierarchical clustering have been developed [20] Recently, we have developed a method for estimating the cluster number in the hierarchical clustering, by considering the following application of the graphical model to the clusters [10] In our approach, the variance inflation factor (VIF) is adopted as a stopping rule, and is defined by − VIFi = rii , Step Calculate the partial correlation coefficient matrix P(τ) from the correlation coefficient matrix C(τ) τ indicates the number of the iteration (3) − where rii is the ith diagonal element of the inverse of the correlation coefficient matrix between explanatory variables [21] In the cluster number determination, the popular cutoff value of 10.0 [21] was adopted as a threshold in the present analysis, also with reference to the previous analyses After the cluster number determination, the average expression profiles are calculated for the members of each cluster, and then the average correlation coefficient matrix between the clusters is calculated from them Finally, the average correlation coefficient matrix between the clusters is subjected to the graphical Gaussian modeling Note that the average coefficient correlation matrix avoids the difficulty of the above numerical calculation, due to the distinctive patterns of the average expression profiles of clusters This means that the GGM works well for the average coefficient correlation matrix 2.2.2 Graphical Gaussian modeling The concept of conditional independence is fundamental to graphical Gaussian modeling (GGM) The conditional independence structure of the data is characterized by a conditional independence graph In this graph, each variable is represented by a vertex, and two vertices are connected by an edge if there is a direct association between them In contrast, a pair of vertices that are not connected in the graph is conditionally independent In the procedure for applying the GGM to the profile data [11], a graph, G = (V , E), is used to represent the relationship among the M clusters, where V is a finite set of nodes, each corresponding to one of the M clusters, and E is a finite set of edges between the nodes E consists of the edges between cluster pairs that are conditionally dependent The conditional independence is estimated by the partial correlation coefficient, expressed by ri j ri, j |rest = − √ ii √ j j , r r Step Prepare a complete graph of G(0) = (V , E) The nodes correspond to M clusters All of the nodes are connected G(0) is called a full model Based on the expression profile data, construct an initial correlation coefficient matrix C(0) (4) where ri j |rest is the partial correlation coefficient between variables i and j, given the rest variables, and ri j is the (i, j) element in the reverse of the correlation coefficient matrix In order to evaluate which pair of clusters is conditionally independent, we applied the covariance selection [22], which was attained by the stepwise and iterative algorithm developed by Wermuth and Scheidt [23] The algorithm is presented as Algorithm The graph obtained by the above procedure is an undirected graph, which is called an independence graph The in- Step Find an element that has the smallest absolute value among all of the nonzero elements of P(τ) Then, replace the element in P(τ) with zero Step Reconstruct the correlation coefficient matrix, C(τ + 1), from P(τ) In C(τ + 1), the element corresponding to the element set to zero in P(τ) is revised, while all of the other elements are left to be the same as those in C(τ) Step In the Wermuth and Sheidt algorithm, the termination of the iteration is judged by the “deviance” values Here, we used two types of deviance, dev1 and dev2, with the following: dev1 = Nc log dev2 = Nc log C(τ + 1) C(0) C(τ + 1) C(τ) , (5) Calculate dev1 and dev2 The two deviances follow an asymptotic χ distribution with a degree of freedom = n, and that with a degree of freedom = 1, respectively n is the number of elements that are set to zero until the (τ + 1)th iteration In our approach, n is equal to (τ + 1) |C(τ)| indicates the determinant of C(τ) Nc is the number of different conditions under which the expression levels of M clusters are measured Step If the probability value corresponding to dev1 ≤ 0.05, or the probability value corresponding to dev2 ≤ 0.05, then the model C(τ + 1) is rejected, and the iteration is stopped Otherwise, the edge between a pair of clusters with a partial correlation coefficient set to zero in P(τ) is omitted from G(τ) to generate G(τ + 1), and τ is increased by Then, go to Step Algorithm dependence graph represents which pair of clusters is conditionally independent That is, when the partial correlation coefficient for a cluster pair is equal to 0, the cluster pair is conditionally independent, and the relationship is expressed as no edge between the nodes corresponding to the clusters in the independence graph The genes grouped into each cluster are expected to share similar biological functions, in addition to the regulatory mechanism [24] Thus, a network between the clusters can be approximately regarded as a network between gene systems, each with similar functions, from a macroscopic viewpoint Note that the number of connections in one vertex is not limited, while it is only one in the cluster analysis This EURASIP Journal on Bioinformatics and Systems Biology feature of the network reflects the multiple relationships of a gene or a gene group in terms of the biological function 2.2.3 Rearrangement of the inferred network When there are many edges, drawing them all on one graph produces a mess or “spaghetti” pattern, which would be difficult to read Indeed, in some examples of the application of GGM to actual profiles, the intact networks by GGM still showed complicated forms with many edges [11, 16] Since the magnitude of the partial correlation coefficient indicates the strength of the association between clusters, the intact network can be rearranged according to the partial correlation coefficient value, to interpret the association between clusters The strength of the association can be assigned by a standard test for the partial correlation coefficient [25] By Fisher’s Z transformation of partial correlation coefficients, that is, Z= + ri j ·rest , log − ri j ·rest (6) Z is approximately distributed according to the following normal distribution: N + ri j ·rest 1 , , log − ri j ·rest Nc − (M − 2) − The inferred network can be statistically evaluated in terms of the gene-gene interactions The chance probability was estimated by the correspondence between the inferred cluster network and the information about gene interactions The following steps were used (1) The known gene pairs with interactions in the database were overlaid onto the inferred network (2) The number of cluster pairs, upon which the gene interactions were overlaid, was counted (3) The chance probability, in which the cluster pairs connected by the established edges in the network were found in all possible pairs, was calculated by using the following equation: P =1− i=0 N −g n−i N n , The inferred network can be evaluated in terms of the biological knowledge For this purpose, we characterize the clusters by GO terms, and overlay the knowledge about the gene interactions onto the network For this purpose, we first use GO::TermFinder [18] to characterize the clusters by GO terms with the user-defined significance probability (http://search.cpan.org/dist/GO-TermFinder) Then, Pathway Studio [19] is used to survey the biological information about the gene interactions between the selected genes 2.5 Software All calculations of the present clustering and GGM were performed by the ASIAN web site [26, 27] (http://www.eureka cbrc.jp/asian) and “Auto Net Finder,” the commercialized PC version of ASIAN, from INFOCOM CORPORATION, Tokyo, Japan (http://www.infocom.co.jp/bio/download) 2.6 Expression profile data The expression profiles of 8516 genes were monitored in 27 CHC samples and 17 HCC samples [28] 2.3 Statistical significance of the inferred network with the biological knowledge g i Evaluation of the inferred network in terms of the biological knowledge (7) where Nc and M are the number of conditions and the number of clusters, respectively Thus, we can statistically test the observed correlation coefficients under the null hypothesis with a significance probability f −1 2.4 (8) where N is the number of possible cluster pairs in the network, n is the number of cluster pairs with edges in the inferred network, f is the number of cluster pairs with edges in the inferred network, including the known gene pairs with interactions, and g is the number of cluster pairs, including the known gene pairs with interactions 3.1 RESULTS AND DISCUSSION Clustering Among the 8516 genes with expression profiles that were measured in the previous studies [28], 661 genes were selected as those characteristically expressed in the CHC and HCC stages As a preprocessing step for the association inference, the genes thus selected were automatically divided into 18 groups by ASIAN [26, 27] Furthermore, each cluster was characterized in terms of the GO terms, which define the macroscopic features of the cluster in terms of the biological function Figure shows the dendrogram of clusters, together with their expression patterns As seen in Figure 1, the genes were grouped into 18 clusters, in terms of the number of members and the expression patterns in the clusters The average number of cluster members was 36.7 genes (SD, 14.2), and the maximum and minimum numbers of members were 69 in cluster 14 and 18 in cluster 9, respectively As for the expression pattern, five clusters (10, 12, 14, 15, and 18) and ten clusters (1–7, 9, 16, and 17) were composed of up- and downregulated genes, respectively, and three clusters (8, 11, and 13) showed similar mixtures of up- and downregulated genes Table shows the GO terms for the clusters (clusterGOB), which characterized them well (see details at http://www.cbrc.jp/∼horimoto/HCGO.pdf) Among the 661 genes analyzed in this study, 525 genes were characterized by the GO terms, and among the 18 clusters, 11 clusters were characterized by GO terms with P < 05 In addition, 188 genes (28.3% of all characterized genes) corresponded to the GO terms listed in Table As seen in the table, although Sachiyo Aburatani et al most clusters are characterized by several GO terms, reflecting the fact that the genes function generally in multiple pathways, the clusters are not composed of a mixture of genes with distinctive functions For example, cluster is characterized by 10 terms, and most of the terms are related to the energy metabolism Thus, the GO terms in the respective clusters share similar features of biological functions, which cause the hierarchical structure of the GO term definitions In Table 1, most of the clusters characterized by GO terms with P < 05 are related to response function and to metabolism Clusters 1, 6, 8, 12, and 13 are characterized by GO terms related to different responses, and clusters 2, 3, 4, and are characterized by GO terms related to different aspects of metabolism Although the genes in two clusters, 14 and 16, did not adhere to this dichotomy, the genes characteristically expressed in HCC in the above nine clusters were related to the responses and the metabolic pathways As for the remaining clusters with lower significance, three clusters (9, 10, and 11) were also characterized by response functions, and four clusters (5, 15, 17, and 18) were related to morphological events at the cellular level Note that none of the clusters characterized by cellular level events attained the significance level This may be because the genes related to cellular level events represent only a small fraction of genes relative to all genes with known functions, in comparison with the genes related to molecular level events in the definition of GO terms It is interesting to determine the correspondence between the up- and downregulated genes and the GO terms in the clusters In the five clusters of upregulated genes, clusters 10 and 12 were characterized by different responses, and two clusters were characterized by morphological events, which were the categories of “cell proliferation” in cluster 15 and of “development” in cluster 18 The remaining cluster, 14, was characterized by regulation, development, and metabolism As for the clusters of downregulated genes, four of the ten clusters were characterized by GO terms related to various aspects of metabolism In the remaining six clusters, three clusters were characterized by GO terms related to responses, two clusters were characterized by morphological events, and one cluster was characterized by mixed categories In summary, the present gene selection and the following automatic clustering produced a macroscopic view of gene expression in hepatocellular carcinogenesis Although the clusters contain many genes that not always share the same functions, the clusters were characterized by their responses, morphological events, and metabolic aspects from a macroscopic viewpoint The clusters of upregulated genes were characterized by the former two categories, and those of the downregulated genes represented all three categories Thus, the present clustering serves to interpret the network between the clusters in terms of the biological function and the gene expression pattern 3.2 Known gene interactions in the inferred network The association between the 18 clusters inferred by GGM is shown in Figure In the intact network by ASIAN, 96 of 153 possible edges between 18 clusters (about 63%) were estab- 10 (38) 11 (31) 12 (30) 13 (56) (32) (18) (25) (24) 17 (24) 14 (69) 15 (28) 18 (28) 16 (50) (42) (48) (32) (59) (27) Figure 1: Dendrogram of genes and profiles The dendrogram was constructed by hierarchical clustering with the metric of the Euclidian distances between the correlation coefficients and the UPGMA The blue line on the dendrogram indicates the cluster boundary estimated automatically by ASIAN The gene expression patterns of the respective clusters in the CHC and HCC stages are shown by the degree of intensity: the red and green colors indicate relatively higher and lower intensities The cluster number and the number of member genes in each cluster (in parentheses) are denoted on the right side of the figure lished by GGM Since the intact network is still messy, the network was rearranged to interpret its biological meaning by extracting the relatively strong associations between the clusters, according to the procedure in Section 2.2.3 After the rearrangement, 34 edges remained by the statistical test of the partial correlation coefficients with 5% significance In the rearranged network, all of the clusters were nested, but each cluster was connected to a few other clusters Indeed, the average number of edges per cluster was 2.3, and the maximum and minimum numbers of edges were seven in cluster 15 and one in cluster 9, respectively In particular, the numbers of edges are not proportional to the numbers of constituent genes in each cluster For example, while the numbers of genes in clusters 15 and 17 are equal to each other (24 genes), the number of edges from cluster 15 (2 edges) differs from that from cluster 17 (5 edges) Thus, the number of edges does not depend on the number of genes belonging to the cluster, but rather on the gene associations between the cluster pairs 6 To test the validity of the inferred network in terms of biological function, the biological knowledge about the gene interactions is overlaid onto the inferred network For this purpose, all of the gene pairs belonging to cluster pairs are surveyed by Pathway Assist, which is a database for biological knowledge about molecular interactions, compiled based on the gene ontology [17] Among the 661 genes analyzed in this study, the interactions between 90 gene pairs were detected by Pathway Assist, and 50 of these pairs were found in Figure Notice that the number of gene pairs reported in the literature does not directly reflect the importance of the gene interactions, and instead is highly dependent on the number of scientists who are studying at the corresponding genes Thus, we counted the numbers of cluster pairs in which at least one gene pair was known, by projecting the gene pairs with known interactions onto the network By this projection, the interactions were found in 35 (g in the equation of Section 2.3) cluster pairs among 153 (N) possible pairs (see details of the gene pair projection at http://www.cbrc.jp/∼horimoto/GPPN.pdf) Then, 19 ( f ) of the 35 cluster pairs were overlapped with 34 (n) cluster pairs in the rearranged network The chance probability that a known interaction was found in the connected cluster pairs in the rearranged network was calculated as P < 10−4.3 Thus, the rearranged network faithfully captures the known interactions between the constituent genes Furthermore, the genes with known interactions were corresponded to the genes responsible for the GO terms of each cluster, as shown in Table The genes responsible for the GO terms were distributed over all cluster pairs, including gene pairs with known interactions, except for only two pairs, clusters 15 and 17, and 15 and 18 Thus, the network can be interpreted not only by the known gene interactions but also by the GO terms characterizing the clusters 3.3 Gene systems network characterized by GO terms 3.3.1 Coarse associations between the clusters To elucidate the associations between the clusters, the cluster associations with 1% significance probability were further discriminated from those with 5% probability This generated four groups of clusters, shown in Figure 3(a) First, we will focus on the groups including the clusters that were characterized by GO terms with a significance probability, and that were definitely occupied by upor downregulated genes (clusters depicted by triangles with bold lines in the figure) Groups I and III attained the above criteria In group I, the clusters were a mixture of the clusters of the up- and downregulated genes Note that three of the six clusters were composed of upregulated genes, which were characterized by responses (cluster 12), mixed categories (cluster 14), and morphological events (cluster 15) In group III, all three clusters were of downregulated genes One cluster was characterized by responses, and two were characterized by amino-acid-related metabolism In contrast, groups II and IV were composed of the clusters that were somewhat inadequately characterized by GO terms and expression patterns Thus, groups I and III provide the characteristic fea- EURASIP Journal on Bioinformatics and Systems Biology tures about the orchestration of gene expression in hepatocellular carcinogenesis Secondly, a coarse grinning for group associations provides another viewpoint, shown in Figure 3(b) When the groups with at least one edge between the clusters in the respective groups were presented, regardless of the number of edges, groups I, II, and IV were nested, and group III was connected with only group I In the second view, group I, which includes three of the five clusters of upregulated genes in all clusters, was associated with all of the other groups This suggests that group I represents a positive part of the gene expression in hepatocellular carcinogenesis, which is consistent with the interpretation by the first view, from the significant GO terms and the clear expression patterns Interestingly, among the clusters characterized by morphological events (clusters 5, 15, 17, and 18), three of the four clusters were distributed over groups I, II, and IV, and the distribution was consistent with the nested groups This suggests that the upregulated genes of the clusters in group I are responsible for the events at the cellular level Thirdly, the clusters not belonging to the four groups were clusters 1, 3, and Clusters 1, 3, and were directly connected with groups I, III, and IV, groups I and III, and group IV, respectively Interestingly, cluster 1, characterized by only “anti-inflammatory response,” was connected with five clusters belonging to three groups, in which four clusters were downregulated clusters Although cluster was not clearly characterized by the GO terms, cluster was characterized by metabolic terms that were quite similar to those for cluster 2, a downregulated cluster Thus, the three clusters may be concerned with downregulation in hepatocellular carcinogenesis 3.3.2 Interpretations of the inferred network in terms of pathogenesis The coarse associations between the clusters in the preceding section can be interpreted on the macroscopic level, such as the pathological level The interpretation of the network inferred based on the information at the molecular level will be useful to bridge the gap between the information about the disease mechanisms at the molecular and more macroscopic levels One of the most remarkable associations is found in group I Cluster 12, with upregulation, was associated at a 1% significance level with cluster 2, with downregulation The former cluster is characterized by the GO terms related to the immune response, and the latter is characterized by those involved with metabolism In general, CHC and HCC result in serious damage to hepatocytes, which are important cells for nutrient metabolism, and the damage induces different responses Indeed, HCC is a suitable target for testing active immunotherapy [29] Furthermore, cluster was also associated at a 1% significance level with cluster 14, characterized by prostaglandin-related terms This may reflect the fact that one mediator of inflammation, prostaglandin, shows elevated expression in human and animal HCCs [30] Thus, the associations in group I are involved in the molecular pathogenesis of the CHC and HCC stages Sachiyo Aburatani et al Table 1: Cluster characterization by GO terms# GO no Category P-value Fraction 2 2 2 2 2 GO:0030236 GO:0006094 GO:0006066 GO:0006091 GO:0019319 GO:0046165 GO:0046364 GO:0006067 GO:0006069 GO:0006629 GO:0009618 Anti-inflammatory response Gluconeogenesis Alcohol metabolism Generation of precursor metabolites and energy Hexose biosynthesis Alcohol biosynthesis Monosaccharide biosynthesis Ethanol metabolism Ethanol oxidation Lipid metabolism Response to pathogenic bacteria 0.18% 0.06% 0.12% 0.14% 0.34% 0.34% 0.34% 0.48% 0.48% 1.47% 4.96% of 22/6 of 26081 of 37/19 of 26081 of 37/312 of 26081 of 37/961 of 26081 of 37/33 of 26081 of 37/33 of 26081 of 37/33 of 26081 of 37/5 of 26081 of 37/5 of 26081 of 37/722 of 26081 of 37/15 of 26081 3 3 GO:0006094 GO:0019319 GO:0046165 GO:0046364 GO:0009069 Gluconeogenesis Hexose biosynthesis Alcohol biosynthesis Monosaccharide biosynthesis Serine family amino acid metabolism 0.61% 1.87% 1.87% 1.87% 4.49% of 15/19 of 26081 of 15/33 of 26081 of 15/33 of 26081 of 15/33 of 26081 of 15/51 of 26081 4 4 4 GO:0006725 GO:0009308 GO:0006570 GO:0050878 GO:0006950 GO:0006519 GO:0007582 Aromatic compound metabolism Amine metabolism Tyrosine metabolism Regulation of body fluids Response to stress Amino acid and derivative metabolism Physiological process 0.07% 0.38% 0.59% 1.65% 2.70% 4.12% 4.63% of 20/140 of 26081 of 20/454 of 26081 of 20/11 of 26081 of 20/113 of 26081 of 20/1116 of 26081 of 20/398 of 26081 20 of 20/17195 of 26081 5 GO:0006917 GO:0012502 Induction of apoptosis∗ Induction of programmed cell death∗ 16.06% 16.06% 6 6 6 6 6 GO:0009613 GO:0043207 GO:0006950 GO:0009605 GO:0006953 GO:0006955 GO:0006956 GO:0006952 GO:0050896 GO:0009607 GO:0006629 Response to pest, pathogen, or parasite Response to external biotic stimulus Response to stress Response to external stimulus Acute-phase response Immune response Complement activation Defense response Response to stimulus Response to biotic stimulus Lipid metabolism 0.00% 0.00% 0.00% 0.05% 0.05% 0.34% 0.48% 0.68% 1.15% 1.65% 2.20% of 29/522 of 26081 of 29/557 of 26081 10 of 29/1116 of 26081 10 of 29/1488 of 26081 of 29/25 of 26081 of 29/1098 of 26081 of 29/52 of 26081 of 29/1209 of 26081 11 of 29/2619 of 26081 of 29/1372 of 26081 of 29/722 of 26081 7 7 7 7 GO:0006559 GO:0019752 GO:0006082 GO:0006558 GO:0009074 GO:0006519 GO:0019439 GO:0006629 GO:0009308 L-phenylalanine catabolism Carboxylic acid metabolism Organic acid metabolism L-phenylalanine metabolism Aromatic amino acid family catabolism Amino acid and derivative metabolism Aromatic compound catabolism Lipid metabolism Amine metabolism 0.83% 1.00% 1.02% 1.26% 1.26% 1.67% 1.79% 3.04% 3.09% of 31/9 of 26081 of 31/590 of 26081 of 31/592 of 26081 of 31/11 of 26081 of 31/11 of 26081 of 31/398 of 26081 of 31/13 of 26081 of 31/722 of 26081 of 31/454 of 26081 8 GO:0001570 GO:0006950 GO:0050896 Vasculogenesis Response to stress Response to stimulus 0.09% 0.42% 2.33% of 21/4 of 26081 of 21/1116 of 26081 of 21/2619 of 26081 Cluster no of 13/132 of 26081 of 13/132 of 26081 EURASIP Journal on Bioinformatics and Systems Biology Table 1: Continued ∗ GO:0009611 Response to wounding 11.19% of 13/394 of 26081 10 GO:0009607 Response to biotic stimulus∗ 6.66% of 19/1372 of 26081 11 GO:0050896 Response to stimulus∗ 72.68% of 17/2619 of 26081 12 12 12 12 12 12 12 12 12 GO:0006955 GO:0006952 GO:0050874 GO:0009607 GO:0050896 GO:0030333 GO:0019882 GO:0019884 GO:0019886 Immune response Defense response Organismal physiological process Response to biotic stimulus Response to stimulus Antigen processing Antigen presentation Antigen presentation, exogenous antigen Antigen processing, exogenous antigen via MHC class II 0.01% 0.01% 0.02% 0.03% 0.39% 0.97% 2.62% 3.97% 4.22% of 18/1098 of 26081 of 18/1209 of 26081 10 of 18/2432 of 26081 of 18/1372 of 26081 of 18/2619 of 26081 of 18/108 of 26081 of 18/151 of 26081 of 18/32 of 26081 of 18/33 of 26081 13 13 13 13 13 13 GO:0009611 GO:0009613 GO:0043207 GO:0006955 GO:0006950 GO:0050874 Response to wounding Response to pest, pathogen, or parasite Response to external biotic stimulus Immune response Response to stress Organismal physiological process 0.08% 0.38% 0.55% 3.12% 3.44% 3.98% of 30/394 of 26081 of 30/522 of 26081 of 30/557 of 26081 of 30/1098 of 26081 of 30/1116 of 26081 10 of 30/2432 of 26081 14 14 14 14 14 GO:0051244 GO:0007275 GO:0001516 GO:0046457 GO:0051242 Regulation of cellular physiological process Development Prostaglandin biosynthesis Prostanoid biosynthesis Positive regulation of cellular physiological process 0.51% 0.94% 3.30% 3.30% 4.35% of 45/665 of 26081 13 of 45/2060 of 26081 of 45/9 of 26081 of 45/9 of 26081 of 45/289 of 26081 15 GO:0008283 Cell proliferation∗ 29.37% 16 16 16 16 GO:0042221 GO:0008152 GO:0009628 GO:0006445 Response to chemical substance Metabolism Response to abiotic stimulus Regulation of translation 0.16% 1.29% 1.89% 2.82% 17 GO:0050817 Coagulation∗ 13.92% of 12/118 of 26081 11.67% of 16/2060 of 26081 18 GO:0007275 ∗ Development of 26/488 of 26081 of 31/237 of 26081 25 of 31/11891 of 26081 of 31/400 of 26081 of 31/87 of 26081 # The gene ontology terms in each cluster, detected with 5% significance probability by using GO::TermFinder [18], are listed When the terms with that significance probability were not found in the cluster, the terms with the smallest probability were listed as indicated by an asterisk In the last column, “Fraction,” the numbers of genes belonging to the corresponding category in the cluster, of genes belonging to the cluster, of genes belonging to the corresponding category in all genes of the GO term data set, and of all genes are listed The associated clusters and in group III, which were characterized by GO terms related to amino acid and lipid metabolism, also show downregulation Indeed, the products of dysregulated (aberrant regulation) metabolism are widely used to examine liver function in common clinical tests [8] In addition, the connection between the clusters in groups III and I implies that the downregulation of the clusters in group III may be related to abnormal hepatocyte function In addition, cluster 15 in group I, which is characterized by the GO term “proliferation,” was associated with different clusters in groups I, II, and IV It is known that abnormal proliferation is one of the obvious features of cancer [31] This broad association may be responsible for the cellular level events in hepatocellular carcinogenesis In summary, the inferred network reveals a coarse snapshot of the gene systems related to the molecular pathogenesis and clinical characteristics of hepatocellular carcinogenesis Although the resolution of the network is still low, due to the cluster network, the present network may provide some clues for further investigations of the pathogenic relationships involved in hepatocellular carcinoma 3.3.3 Interpretations of the inferred network in terms of gene-gene interactions In addition to the macroscopic interpretations above, the gene functionality from the gene-gene interactions listed in Figure is also discussed in the context of hepatocellular carcinoma Although the consideration of genegene interactions is beyond the aim of the present study, Sachiyo Aburatani et al ALB-MTP CYP2C9-CYP2C18 PLG-CPB2 THBD-CPB2 TF-CDH1 TF-HPX GNG5-AEBP1 PRELP-SPARC COL1A2-RFX5 CYP2E1-COL1A2 ALB-OCRL 12 FBP1- MAN1A1 LPA-MAP2K1 CYP2E1-MAP2K1 ALB-BCHE IGFBP3-IRS1 14 15 13 SDC2-CXCL12 16 MAOA-MAOB BAAT-NAT2 MAGED1-BIRC4 B2M- ARAF1 B2M-TIMP1 F8-VWF ZFP36-VWF B2M-RFX5 HTATIP2-NME2 SHC1-MAP3K10 DNCH1-CDKN2A 18 ASCL1-BMP4 CITED2-CDKN2A 11 FOS-ODC1 PCK1-PCK2 PLG-SERPINF2 THBD-SERPINF2 PLG-KLKB1 SPINK1-CTSB FOXA3-CYP3A4 10 AMBP-MAP2K1 CRAT-AR SORL1-CSF2 DIABLO-HSPB1 17 VEGF-A2M NTRK2-A2M JUN-A2M VEGF-HSPB1 VEGF-THBS2 VEGF-CTF1 VEGF-CSF2 JUN-CSF2 JUN-WEE1 Figure 2: Network between clusters, together with a projection of biological knowledge about the gene interactions The clusters are indicated by triangles and circles, in which the cluster numbers correspond to those in Figure 1, and the edges between the clusters are associations with 5% significance probability The red triangles, the green upside-down triangles, and the circles indicate the clusters of up- and downregulated genes, and the mixture of them, respectively, and the dotted triangles indicate the clusters that were not characterized by GO terms with less than 5% significance probability The known gene interactions in Pathway Assist are indicated between the clusters, in which the genes highlighted by bold letters are characterized by the GO terms in Table some examples may provide possible clues about the disease mechanisms First, we surveyed the frequencies of GO terms (geneGOB listed in the supplemental data at http://www.cbrc jp/∼horimoto/suppl/HCGO.pdf) in the selected genes in the present analysis, to investigate the features of gene-gene interactions in the inferred network A few general terms appeared frequently, such as “response” (122 times in the geneGOB column of the supplemental data at http://www.cbrc.jp/∼horimoto/suppl/HCGO.pdf) and “metabolism” (183), as expected from the coarse associations between the clusters in the preceding section As for more specific terms about the gene function, “lipid” (46), “apoptosis” (31), and “cell growth” (27) are remarkably found in the list The “lipid” is expected from the relationship between groups I and III, and the “apoptosis” and the “cell growth” are also expected from the frequent appearance of GO terms (clusterGOB listed in Table 1) related to the morphological events Since the frequent appearance of “lipid” may be a sensitive reflection of the protein-protein interactions in lipid metabolic pathways to the expression profiles, here, we focus on the gene-gene interactions characterized by the “apoptosis” and the “cell growth.” Among the gene-gene interactions listed in Figure 2, the gene-gene interactions characterized by the cell growth or death are found in the coarse associations between the clus- ters Group I contains the gene-gene interactions related to apoptosis The expression of HTAIP2 (HIV-1 Tat interactive protein 2, 30 kd) in cluster 14 induces the expression of a number of genes, including NME2 (nonmetastatic cells 2, protein) in cluster 15 as well as the apoptosis-related genes Bad and Siva [32] MAGED1 (melanoma antigen, family D, 1) in cluster 13, and its binding partner BIRC4 (baculoviral IAP repeat-containing 4) in cluster 14 are known to play some roles in apoptosis [33] In addition, the expression of COL1A2 (collagen, type I, alpha 2) in cluster 12, which is related to cell adhesion and skeletal development, is regulated by RFX5 (regulatory factor X, 5) in cluster 14 [29, 34] In group IV, the expression of CSF2 (colony-stimulating factor 2) in cluster is dependent on the cooperation between NFAT (nuclear factor of activated T cells) and JUN (Jun oncogene) in cluster 10 [35] Between groups I and II, ASCL1 (achaete-scute complex-like 1) in cluster 13 and BMP4 (bone morphogenetic protein 4) in cluster 18 share the function of cell differentiation [36] As a result, the gene-gene interactions listed above are related to the mechanisms of cell growth or death at the molecular level On the other hand, the cluster associations reveal the relationship between the cancer-induced events and various aspects of metabolisms at the pathogenesis and clinical characteristics Thus, the metabolic pathways might directly 10 EURASIP Journal on Bioinformatics and Systems Biology Group I 12 14 Group III 15 13 Group II 16 18 study, our aim was not the inference of detailed gene-gene interactions, but of coarse gene system interactions Indeed, the use of a partial correlation coefficient is employed as a feasible approach for gene association inference as a first approximation in some studies [37, 38] Thus, the assumption of the linearity is not suitable for a fine analysis of dynamic gene behaviors, but may be useful for the approximate analysis of static gene associations 11 10 ACKNOWLEDGMENTS S Aburatani was supported by a Grant-in-Aid for Scientific Research (Grant 18681031) from the Ministry of Education, Culture, Sports, Science, and Technology of Japan, and K Horimoto was partly supported by a Grant-in-Aid for Scientific Research on Priority Areas “Systems Genomics” (Grant 18016008) and by a Grant-in-Aid for Scientific Research (Grant 19201039) from the Ministry of Education, Culture, Sports, Science, and Technology of Japan This study was supported in part by the New Energy and Industrial Technology Development Organization (NEDO) of Japan and by the Ministry of Health, Labour, and Welfare of Japan 17 Group IV (a) I III II IV (b) Figure 3: Orchestration of gene systems (a) The association with 1% significance probability is indicated by a bold line, and the clusters with 1% significance association are naturally divided into four groups, which are enclosed by broken lines (b) The connections between the groups are drawn schematically, as a coarse grinning of the cluster association influence the mechanisms of cancer-induced cell growth or death at the molecular level in unknown ways 3.4 Merits and pitfalls of the present approach The present analysis reveals a framework of gene system associations in hepatocellular carcinogenesis The inferred network provides a bridge between the events at the molecular level and those at macroscopic levels: the associations between clusters characterized by cancer-related responses and those characterized by metabolic and 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the gene systems related to the molecular pathogenesis and clinical characteristics of hepatocellular